{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8929a5f8",
   "metadata": {},
   "source": [
    "## \"DATA ACQUISITION SYSTEM FOR MEASUREMENT OF PRESSURE DUE TO COMBUSTION OF FLAMMABLE GAS: A DESIGN APPROACH\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2abd30c1",
   "metadata": {},
   "source": [
    "The graph obtained below is the approximation of the actual plot that will be obtained after the data acquisition process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e94ad16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Constants\n",
    "duration = 10.0  # Duration of the process in seconds\n",
    "num_steps = 200  # Number of time steps\n",
    "amplification_factor = 10  # Amplification factor for the pressure signal\n",
    "\n",
    "# Calculate time array\n",
    "time = np.linspace(0, duration, num_steps)\n",
    "\n",
    "# Generate pressure values (exponentially increasing and then decreasing)\n",
    "pressure_increase = np.exp(time[:int(num_steps/2)])\n",
    "pressure_decrease = np.exp(duration - time[int(num_steps/2):])\n",
    "pressure = np.concatenate((pressure_increase, pressure_decrease))\n",
    "\n",
    "# Amplify the pressure values\n",
    "amplified_pressure = pressure * amplification_factor\n",
    "\n",
    "# Simulate data acquisition\n",
    "data = np.random.normal(amplified_pressure, 0.1)  # Adding random noise to simulate measurement variations\n",
    "\n",
    "# Plot the acquired data\n",
    "plt.plot(time, data)\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Amplified Pressure (Acquired)')\n",
    "plt.title('Acquired Pressure vs. Time')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de340fb3",
   "metadata": {},
   "source": [
    "'duration' represents the duration of the process in seconds, 'num_steps' represents the number of time steps, and amplification_factor represents the 'amplification factor' applied to the pressure signal.<br>\n",
    "\n",
    "The 'time' array is created using 'np.linspace()' function, which generates evenly spaced numbers over a specified interval. In this case, it generates 'num_steps' points between 0 and 'duration', representing the time values.<br>\n",
    "\n",
    "The pressure values are generated using exponential functions. The first half of the time points ('time[:int(num_steps/2)]') corresponds to an exponentially increasing pressure, and it is generated using 'np.exp()' function applied to the time array. <br>\n",
    "\n",
    "The second half (time[int(num_steps/2):]) corresponds to an exponentially decreasing pressure, and it is also generated using np.exp() function applied to the difference between duration and the time array. These two arrays are concatenated using np.concatenate() function to create the pressure array. <br>\n",
    "\n",
    "The pressure values are then amplified by multiplying them with the amplification_factor, resulting in the amplified_pressure array. <br>\n",
    "\n",
    "To simulate data acquisition with measurement variations, random noise is added to the amplified_pressure array using np.random.normal(). This function generates random numbers from a normal distribution with a mean of amplified_pressure and a standard deviation of 0.1. The resulting values are stored in the data array. <br>\n",
    "\n",
    "Finally, the acquired data is plotted using plt.plot(). The time array is plotted on the x-axis, and the data array (the acquired pressure values) is plotted on the y-axis. The x-axis is labeled as \"Time (s),\" the y-axis is labeled as \"Amplified Pressure (Acquired),\" and the title of the graph is set as \"Acquired Pressure vs. Time.\" The grid is enabled with plt.grid(True), and plt.show() is called to display the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a4760a0",
   "metadata": {},
   "source": [
    "The below plot will continuously take the reading and it will plot it accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3075be86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_20608/3725247493.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Gas Pressure Over Time'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpause\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.01\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# Adjust the pause duration as needed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;31m# Stop the loop after a certain condition (optional)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mpause\u001b[1;34m(interval)\u001b[0m\n\u001b[0;32m    555\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    556\u001b[0m             \u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 557\u001b[1;33m         \u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    558\u001b[0m         \u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_event_loop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    376\u001b[0m     \"\"\"\n\u001b[0;32m    377\u001b[0m     \u001b[0m_warn_if_gui_out_of_main_thread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 378\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_backend_mod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    379\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    380\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib_inline\\backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(close, block)\u001b[0m\n\u001b[0;32m     39\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m             display(\n\u001b[0m\u001b[0;32m     42\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m                 \u001b[0mmetadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\display.py\u001b[0m in \u001b[0;36mdisplay\u001b[1;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[0;32m    318\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    319\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 320\u001b[1;33m             \u001b[0mformat_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    321\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    322\u001b[0m                 \u001b[1;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36mformat\u001b[1;34m(self, obj, include, exclude)\u001b[0m\n\u001b[0;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    181\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m                 \u001b[1;31m# FIXME: log the exception\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\decorator.py\u001b[0m in \u001b[0;36mfun\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m    230\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mkwsyntax\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m                 \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 232\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mcaller\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mextras\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    233\u001b[0m     \u001b[0mfun\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    234\u001b[0m     \u001b[0mfun\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[1;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    222\u001b[0m     \u001b[1;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 224\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    225\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m         \u001b[1;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[0;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2253\u001b[0m                 \u001b[1;31m# force the figure dpi to 72), so we need to set it again here.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2254\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2255\u001b[1;33m                     result = print_method(\n\u001b[0m\u001b[0;32m   2256\u001b[0m                         \u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2257\u001b[0m                         \u001b[0mfacecolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1667\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1669\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1670\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1671\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    506\u001b[0m             \u001b[1;33m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[1;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    507\u001b[0m         \"\"\"\n\u001b[1;32m--> 508\u001b[1;33m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    509\u001b[0m         mpl.image.imsave(\n\u001b[0;32m    510\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"upper\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    404\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[0;32m    405\u001b[0m               else nullcontext()):\n\u001b[1;32m--> 406\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    407\u001b[0m             \u001b[1;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    408\u001b[0m             \u001b[1;31m# don't forget to call the superclass.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     72\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     75\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   2788\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2789\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2790\u001b[1;33m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[0;32m   2791\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[0;32m   2792\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\_api\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[0;32m    429\u001b[0m                          \u001b[1;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    430\u001b[0m                 **kwargs)\n\u001b[1;32m--> 431\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    432\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    433\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2919\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2920\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2921\u001b[1;33m         \u001b[0mmimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2922\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2923\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'axes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1144\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1145\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mticks_to_draw\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1146\u001b[1;33m             \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1148\u001b[0m         \u001b[1;31m# scale up the axis label box to also find the neighbors, not\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    300\u001b[0m         for artist in [self.gridline, self.tick1line, self.tick2line,\n\u001b[0;32m    301\u001b[0m                        self.label1, self.label2]:\n\u001b[1;32m--> 302\u001b[1;33m             \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    303\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    304\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    725\u001b[0m                                           mtext=mtext)\n\u001b[0;32m    726\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 727\u001b[1;33m                     textrenderer.draw_text(gc, x, y, clean_line,\n\u001b[0m\u001b[0;32m    728\u001b[0m                                            \u001b[0mtextobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mangle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    729\u001b[0m                                            ismath=ismath, mtext=mtext)\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py\u001b[0m in \u001b[0;36mdraw_text\u001b[1;34m(self, gc, x, y, s, prop, angle, ismath, mtext)\u001b[0m\n\u001b[0;32m    201\u001b[0m         \u001b[1;31m# We pass '0' for angle here, since it will be rotated (in raster\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    202\u001b[0m         \u001b[1;31m# space) in the following call to draw_text_image).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 203\u001b[1;33m         \u001b[0mfont\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    204\u001b[0m         font.draw_glyphs_to_bitmap(\n\u001b[0;32m    205\u001b[0m             antialiased=mpl.rcParams['text.antialiased'])\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Create an empty plot\n",
    "plt.figure()\n",
    "\n",
    "# Main loop to continuously acquire and plot data\n",
    "while True:\n",
    "    # Simulate acquiring time and pressure data\n",
    "    current_time = np.random.rand()  # Replace with actual time acquisition method\n",
    "    current_pressure = np.random.rand()  # Replace with actual pressure acquisition method\n",
    "    \n",
    "    # Plot the current data\n",
    "    plt.plot(current_time, current_pressure, 'bo')\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Amplified Pressure')\n",
    "    plt.title('Gas Pressure Over Time')\n",
    "    plt.grid(True)\n",
    "    plt.pause(0.01)  # Adjust the pause duration as needed\n",
    "    \n",
    "    # Stop the loop after a certain condition (optional)\n",
    "    if current_time >= 10:  # Replace with your desired condition\n",
    "        break\n",
    "\n",
    "# Display the final plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555bec97",
   "metadata": {},
   "source": [
    "We will get the complete plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b38328e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAx9klEQVR4nO3dd5wV9fX/8deh9yadZem9KS5g70asiCW22BXNN8Ykv0TBFrtiSYyJGoLdxIQkFEVEUWPDDqjsskt1aUvvfWHL+f0xQ3Jdl+Wy7Ozd3ft+Ph48uHPnc2fO3N2dM/OZmfMxd0dERJJXtUQHICIiiaVEICKS5JQIRESSnBKBiEiSUyIQEUlySgQiIklOiUDKlJmdYGY5MdOZZnZC+NrM7EUz22RmX5nZsWY2vyzWIxWPmX1oZtclOg7ZPyWCJBL+YW4ys9rltU537+PuH4aTxwCnAinuPtjdp7t7jyjWGyadm8ws3cx2mtnqcPsvjmBdbmY7zGy7ma0ws9+bWfWyXk95M7PbzOzjYt5vbmZ7zKxvIuKSsqdEkCTMrCNwLODAOQkKowOwxN13lMO6/gj8Evg1cAjQDrgTGBrR+ga4ewPgZOBS4PqiDcysRkTrjtsBJqi/AkeZWaci718MZLj7nLKLTBJJiSB5XAF8AbwEXBk7w8xeMrNnzOyt8Kj2UzNrbWZ/CM8g5pnZYTHtl4RHi1nh/BfNrE5xKw3bnmJm1wLPAUeG67i3mG6ktmY2wczWmdliM7s5Zl7dMM5NZpYFDNrXhppZd+D/gIvd/V133+XuBe7+ibtfFdPuajOba2bbzCzbzG6ImdfczKaY2WYz22hm081sv38v7j4PmA70NbOO4dnCtWa2DHg/XPY14Xo3mdk0M+sQvm9m9oSZrTWzLeHZTN9w3hnh970tPOv4Tfj+VWb2SZHtdzPrGr5+ycz+bGZTzWwHcGJJ33ORbckJY768yKwrgJfNrGn4Ha0Lt2WKmaXs42dyj5n9LWZ673dTI5xubGbPm9mqcPse2Ju0zKyrmX0Ufifrzeyf+/s5yIFRIkgeVwCvhv9OM7NWReb/mOCIuTmwG/gc+DqcHg/8vkj7y4DTgC5A9/Cz++TuzwM3Ap+7ewN3vzt2friTfQOYTXD0fjLwSzM7LWxyd7iuLuF6v5fMijgJWO7uM0uKCVgLnAU0Aq4GnjCzgeG8XwM5QAugFXA7wdlUicysN8GZ1zcxbx8P9CL43s8Nl3VeuOzpwD/Cdj8CjiP4PpsAFwEbwnnPAze4e0OgL2FSidOlwINAQ+AzSv6ei3qZmERgZj2AQ8OYqwEvEpzppQK7gKcOIK6i68kHugKHEXwXe68v3A+8AzQFUoA/lXIdsg9KBEnAzI4h+GP9l7vPAr4j2DnEmuTus9w9F5gE5Lr7K+5eAPyT4I8z1lPuvtzdNxLsZC45yDAHAS3c/T533+Pu2cCzBN0QECSqB919o7svJ+j62ZfmwOrYN8wsJzy6z917BO7ub7r7dx74iGBnc2z4kTygDdDB3fPC6xklJYKvzWwTwU72OYId5F73uPsOd98F3AA87O5z3T0feAg4NIwpj2Bn3ROwsM2qmHh6m1kjd9/k7l+XEEtRr7v7p+5eCPSj5O+5qElAKzM7Kpy+AnjL3de5+wZ3n+DuO919G8HvwfEHEBcA4UHJ6cAvw+9pLfBETEx5BL+/bd09190/2ceipJSUCJLDlcA77r4+nP47PzyiXhPzelcx0w2KtF8e83op0PYgY+wAtA131pvNbDPBkfPeM5e2xaxzXzYQ7MT/y91TCBJEbcAAzOx0M/si7PrZDJwRtgF4DFgEvBN2G43aT/wD3b2pu3dx9zvDne5esXF3AJ6M2caNYTzt3P19giPqp4E1ZjbWzBqFnzs/jG9p2E1y5H7iiVV0/SV9z9/j7juBfwNXmJkRnAm+DGBm9czsL2a21My2Ah8DTezAL5R3AGoCq2Ji+gvQMpx/K8F39JUFd6Fdc4DLl/1QIqjizKwuwdH08RbcObMa+BUwwMwGHMSi28e8TgVWHsSyINhZLXb3JjH/Grr7GeH8VcWsc1/eB1LMLG1fDSy4c2oC8DjQyt2bAFMJk4S7b3P3X7t7Z+Bs4P+Z2cml3LbYM4nlBF08sdtZ190/C9f7R3c/HOhD0EV0S/j+DHcfRrBzfA34V7i8HUC9mO1qHcf6S/qei/Mywe/QqQRnLFPC938N9ACGuHsjgm4tCL/DIr4XJxAb53KC7sjmMTE1cvc+4bavdvfr3b0twRnVM3uvgUjZUCKo+s4FCoDeBH27hxL0V08nOM0vrZ+ZWYqZNSM4ojzYC3hfAVvNbKQFF4arm1lfM9t7UfhfwG3hBcoU4Of7WpC7zyc4ohxnZqfuXR5wVEyzWgRnB+uAfDM7naBfGgAzOyu8SGnAVoLvsOAgtxFgTLgdfcL1NDazC8PXg8xsiJnVJNhx5gIFZlbLzC4zs8bunhcTDwR9/X3M7FALLtjfs5/17+97Ls50YDMwFhjn7nvC9xsSnC1uDn8P7i7+4wB8CxxnZqlm1hi4be+MsPvrHeB3ZtbIzKqZWRczOz78Xi6MuQi9iSCxlcXPQkJKBFXflcCL7r4sPLJa7e6rCbogLrPS39L4d4I/3uzw3wMHE2R4LeJsgkS1GFhP0NfeOGxyL0F30OJwvX/dzyJ/RnAd4fcE3S85BBcdLwKWhX3aNxMkmE0E10wmx3y+G/AesJ3gwvkzMc9DlJq7TwIeIUhSW4E5BP3jEFy0fjaMZylBF9fj4bzLgSXhZ24EfhIubwFwXxjrQqDE/vM4vufiPuPAKwRdOK/EzPoDUDdcxhfA2yUs412Cg4V0YBb/O6vY6wqC5JxFsP3j+V/33iDgSzPbTvAz+oW7Ly5pO+XAWMnXv0R+yMyWANe5+3uJjkVEDp7OCEREkpwSgYhIklPXkIhIktMZgYhIkkt4EawD1bx5c+/YsWOiwxARqVRmzZq13t1bFDev0iWCjh07MnPm/krIiIhILDPb59P46hoSEUlySgQiIklOiUBEJMkpEYiIJDklAhGRJKdEICKS5JQIRESSnBKBiEgFl1dQyDMfLmL28s2RLL/SPVAmIpJM5qzYwsgJ6WSu3MqNx+czoH2TMl+HEoGISAWUm1fAn95fyJiPsmlarxZ/vmwgp/drs/8PloISgYhIBTNzyUZunZBO9rodXHh4Cnee2ZvG9WpGtj4lAhGRCmL77nwee3ser3yxlLaN6/LKNYM5rnuxdeLKlBKBiEgF8NGCddw+MYOVW3Zx5ZEdueW0HtSvXT676EjXYmZDgSeB6sBz7j66yPymwAtAFyAXuMbd50QZk4hIRbJ55x7unzKXCV/n0KVFff59w5GkdWxWrjFElgjMrDrwNHAqkAPMMLPJ7p4V0+x24Ft3H25mPcP2J0cVk4hIRfJWxiruej2TTTv3cNOJXbnppK7UqVm93OOI8oxgMLDI3bMBzGwcMAyITQS9gYcB3H2emXU0s1buvibCuEREEmrt1lx++3omb2eupm+7Rrx8zSD6tG2csHiiTATtgOUx0znAkCJtZgPnAZ+Y2WCgA5ACfC8RmNkIYARAampqVPGKiETK3fn3rBwemJJFbn4hI4f25PpjO1GjemKf7Y0yEVgx73mR6dHAk2b2LZABfAPk/+BD7mOBsQBpaWlFlyEiUuEt37iT2ydlMH3hegZ3bMbo8/vRuUWDRIcFRJsIcoD2MdMpwMrYBu6+FbgawMwMWBz+ExGpEgoKnVc+X8Jj0+ZjwP3D+nDZkA5Uq1bcsXJiRJkIZgDdzKwTsAK4GLg0toGZNQF2uvse4Drg4zA5iIhUeovWbmPkhAxmLd3E8d1b8NB5/WjXpG6iw/qByBKBu+eb2U3ANILbR19w90wzuzGcPwboBbxiZgUEF5GvjSoeEZHykldQyF8++o4//mcR9WpX54mLBnDuoe0IOj4qnkifI3D3qcDUIu+NiXn9OdAtyhhERMpTRs4Wbp2QztxVWzmzfxvuPacPzRvUTnRYJdKTxSIiZSA3r4A/vLeQZ6dnc0j9Wvzl8sM5rU/rRIcVFyUCEZGD9GX2BkZNzGDx+h1clNae28/sReO60RWJK2tKBCIipbQtN49H357PX79YSvtmdXn1uiEc3bV5osM6YEoEIiKl8MH8tdwxMYNVW3O55uhO/Oa07tSrVTl3qZUzahGRBNm0Yw/3T8li4jcr6NayARN+ehQDU5smOqyDokQgIhIHd+fNjFXc/XomW3blcfPJ3fjZiV2oXaP8i8SVNSUCEZH9WLM1lztfm8O7WWvon9KYv103hF5tGiU6rDKjRCAisg/uzr9mLueBN+eyJ7+Q28/oyTVHJ75IXFlTIhARKcayDTsZNTGdz77bwJBOzXjk/P50bF4/0WFFQolARCRGQaHz0mdLeHzafKpXMx4c3pdLBqVWqCJxZU2JQEQktGDNNm4dn863yzdzUs+WPDi8L20aV7wicWVNiUBEkt6e/EL+/OF3PPXBQhrWqcmTFx/KOQPaVtgicWVNiUBEktrs5ZsZOSGdeau3cc6Attx9dm8OqeBF4sqaEoGIJKVdewp44r0FPDc9m5YN6/DcFWmc0rtVosNKCCUCEUk6n3+3gdsmprNkw04uGZzKbWf0pFGdylMkrqwpEYhI0tiam8fot+bx9y+X0eGQevz9+iEc1aXyFYkra5EmAjMbCjxJMELZc+4+usj8xsDfgNQwlsfd/cUoYxKR5PSfuWu4Y9Ic1m7LZcRxnfnVKd2pW6vyl4coC5ElAjOrDjwNnEowkP0MM5vs7lkxzX4GZLn72WbWAphvZq+GYxiLiBy0Ddt3c+8bWUyevZIerRoy5vLDObR9k0SHVaFEeUYwGFjk7tkAZjYOGEYwNvFeDjS04B6tBsBGID/CmEQkSbg7k2ev5N43stiWm8evTunOT0/oQq0aVas8RFmIMhG0A5bHTOcAQ4q0eQqYDKwEGgIXuXth0QWZ2QhgBEBqamokwYpI1bFqyy7unDSH/8xby4D2TXj0/P70aN0w0WFVWFEmguKexPAi06cB3wInAV2Ad81surtv/d6H3McCYwHS0tKKLkNEBIDCQmfcjOU8PHUueYWF3HlmL64+uhPVq3B5iLIQZSLIAdrHTKcQHPnHuhoY7e4OLDKzxUBP4KsI4xKRKmjJ+h2MmpjOF9kbOarLITx8Xj86HFI1i8SVtSgTwQygm5l1AlYAFwOXFmmzDDgZmG5mrYAeQHaEMYlIFZNfUMgLny7md+8soFb1aow+rx8XDWqfNOUhykJkicDd883sJmAawe2jL7h7ppndGM4fA9wPvGRmGQRdSSPdfX1UMYlI1TJv9VZGjk9nds4WTunVigfO7UvrxnUSHValE+lzBO4+FZha5L0xMa9XAj+KMgYRqXp25xfw9Aff8cwHi2hctyZ/uuQwzurfRmcBpaQni0WkUvlm2SZGTkhnwZrtDD+sHXed1Ztm9WslOqxKTYlARCqFnXvy+d07C3jh08W0blSHF65K46SeyVkkrqwpEYhIhffZovWMmpjBso07+ckRqYwc2pOGSVwkrqwpEYhIhbVlVx4PT53LuBnL6dS8Pv8ccQRDOh+S6LCqHCUCEamQ3slczZ2vzWH99t3ccHxQJK5OTRWJi4ISgYhUKOu37+aeyZlMSV9Fz9YNee7KNPqnNEl0WFWaEoGIVAjuzmvfruDeN7LYubuAX5/anRtP6ELN6ioSFzUlAhFJuJWbd3HHpAw+mL+Ow1KDInHdWqlIXHlRIhCRhCksdF79ahmjp86l0OHus3tzxZEdVSSunCkRiEhCZK/bzqgJGXy1ZCPHdG3Ow+f1o32zeokOKykpEYhIucovKOS5TxbzxLsLqF2jGo9e0J8LD09ReYgEUiIQkXKTtXIrt06YzZwVWzmtTyvuH9aXlo1UJC7RlAhEJHK78wt46v1F/PnD72hSrybPXDaQ0/u21llABaFEICKRmrV0IyMnZLBo7XbOH5jCnWf2oqmKxFUoSgQiEokdu/N5bNp8Xv58CW0b1+XlawZzfPcWiQ5LiqFEICJlbvrCddw2MYOcTbu48sgO3DK0Jw1qa3dTUUX6kzGzocCTBCOUPefuo4vMvwW4LCaWXkALd98YZVwiEo0tO/N44M0s/j0rh84t6vPvG49kUMdmiQ5L9iOyRGBm1YGngVMJBrKfYWaT3T1rbxt3fwx4LGx/NvArJQGRyuntOau56/U5bNyxh/87oQs3n9xNReIqiSjPCAYDi9w9G8DMxgHDgKx9tL8E+EeE8YhIBNZuy+WeyZlMzVhN7zaNePGqQfRt1zjRYckBiDIRtAOWx0znAEOKa2hm9YChwE37mD8CGAGQmppatlGKSKm4OxO+XsH9U7LYlVfALaf1YMRxnVUkrhKKMhEUd4Ow76Pt2cCn++oWcvexwFiAtLS0fS1DRMpJzqad3D5pDh8vWEdah6aMPr8/XVs2SHRYUkpRJoIcoH3MdAqwch9tL0bdQiIVXmGh89cvlvLI2/MAuPecPlx+RAeqqUhcpRZlIpgBdDOzTsAKgp39pUUbmVlj4HjgJxHGIiIH6bt12xk5Pp2ZSzdxXPcWPDS8LylNVSSuKthvIgj7738NpLr79WbWDejh7lNK+py755vZTcA0gttHX3D3TDO7MZw/Jmw6HHjH3XcczIaISDTyCgoZ+3E2T/5nIXVrVufxCwdw/sB2Kg9RhZh7yV3uZvZPYBZwhbv3NbO6wOfufmg5xPcDaWlpPnPmzESsWiTpzFmxhVvHp5O1aitn9GvNPef0oWVDFYmrjMxslrunFTcvnq6hLu5+kZldAuDuu0yHAiJVWm5eAU/+ZyFjP86mWf1ajPnJQIb2bZPosCQi8SSCPeFZgAOYWRdgd6RRiUjCzFiykZHj08lev4MLD0/hzjN707hezUSHJRGKJxHcDbwNtDezV4GjgauiDEpEyt/23fk8+vY8Xvl8KSlN6/LXawdzbDcViUsG+00E7v6umX0NHEHwbMAv3H195JGJSLn5aME6bp+Ywcotu7jqqI7ccloP6qtIXNKI566h48KX28L/e5sZ7v5xdGGJSHnYvHMP903JYuLXK+jSoj7jbzySwzuoSFyyiSfl3xLzug5BDaFZwEmRRCQikXN33pqzmt++PofNO/O46cSu3HRSVxWJS1LxdA2dHTttZu2BRyOLSEQitXZrLne9PodpmWvo264RL18zmD5tVSQumZWmEzAH6FvWgYhItNydf8/K4YEpWezOL2TU6T257phO1FCRuKQXzzWCP/G/YnHVgEOB2RHGJCJlbPnGndw2MYNPFq1ncMdmjD6/H51bqEicBOI5I4h9jDcf+Ie7fxpRPCJShgoKnVc+X8Kjb8+nmsH95/blssGpKhIn3xPPNYKXyyMQESlbC9dsY+SEdL5etpkTerTgweH9aNekbqLDkgpon4nAzDIofvwAA9zd+0cWlYiUWl5BIWM+/I4/vb+I+rWr88RFAzj3UBWJk30r6YzgrHKLQkTKREbOFm4ZP5t5q7dxVv823HNOH5o3qJ3osKSC22cicPel5RmIiJRebl4BT7y3gGc/zqZ5g9qMvfxwftSndaLDkkoinruGjgD+BPQCahGMLbDD3RtFHJuIxOHL7A2MmpjB4vU7uHhQe247oxeN66pInMQvnruGniIYXezfQBpwBdA1noWb2VDgSYLk8Zy7jy6mzQnAH4CawHp3Pz6eZYsku225eTzy9jz+9sUy2jery6vXDeHors0THZZUQnE9UObui8ysursXAC+a2Wf7+4yZVQeeBk4leAhthplNdvesmDZNgGeAoe6+zMxalmYjRJLNB/PWcvukDFZvzeXaYzrx6x91p14tFYmT0onnN2enmdUCvjWzR4FVQP04PjcYWOTu2QBmNg4YBmTFtLkUmOjuywDcfe2BBC+SbDbu2MN9b2Ty2rcr6dayARN+ehQDU5smOiyp5Pb5bLmZ7R3S7PKw3U3ADqA9cH4cy24HLI+Zzgnfi9UdaGpmH5rZLDO7It7ARZKJu/PG7JWc+vuPmJK+il+c3I0pNx+jJCBloqQzgmfNrAHwD2Bc2KVz7wEsu7iblos+l1ADOBw4GagLfG5mX7j7gu8tyGwEMAIgNTX1AEIQqfzWbM3ljklzeG/uGvqnNObV64fQs7Xu1ZCyU9Lto4eZWQ+CC8XjzWwP/0sK8dxamkNw9rBXCrCymDbr3X0HsMPMPgYGAN9LBO4+FhgLweD1caxbpNJzd/45YzkPTp3LnvxC7jijF1cf3VFF4qTMlfgb5e7z3f1ed+8NXAk0Ad43s3hqDc0AuplZp/Aaw8XA5CJtXgeONbMaZlYPGALMPdCNEKlqlm7YwWXPfcmoiRn0btOIab88juuP66wkIJGI6zYDM6sGtARaEVwoXre/z7h7vpndBEwjuH30BXfPNLMbw/lj3H2umb0NpAOFBLeYzindpohUfgWFzoufLubxd+ZTo1o1Hhrej4sHtVeROImUue+7p8XMjgUuAc4F5gDjgAnuvqVcoitGWlqaz5w5c/8NRSqZ+au3ceuEdGYv38zJPVvywPC+tGmsInFSNsxslrunFTevpKJzy4FlBDv/e919TUTxiSS1PfmFPPPhIp7+YBEN69TkyYsP5ZwBbVUkTspNSV1Dx6jekEi0Zi/fzK3j05m/ZhvDDm3Lb8/qzSEqEiflTEXnRBJg154Cfv/ufJ7/ZDEtG9bhuSvSOKV3q0SHJUlKz6SLlLPPvlvPbRMzWLphJ5cOSWXU6T1pVEdF4iRxlAhEysnW3DwenjqPf3y1jA6H1OPv1w/hqC4qEieJV9LF4thB63/A3W+OJCKRKui9rDXc8VoG67btZsRxnfnVKd2pW6t6osMSAUo+I9h7j+bRQG/gn+H0hcCsKIMSqSo2bN/NvW9kMXn2Snq2bsjYy9MY0L5JosMS+Z6SLha/DGBmVwEnunteOD0GeKdcohOppNydybNXcs/kTLbvzudXp3Tnpyd0oVYNPRksFU881wjaAg2BjeF0g/A9ESnGqi27uHPSHP4zby2Htm/Coxf0p3urhokOS2Sf4kkEo4FvzOyDcPp44J7IIhKppAoLnX/MWMbDU+eRX1jInWf24uqjO1Fd5SGkgttvInD3F83sLYKCcACj3H11tGGJVC6L1+9g1IR0vly8kaO6HMLo8/qTeki9RIclEpd4Bq834BSgs7vfZ2apZjbY3b+KPjyRii2/oJAXPl3M795ZQK0a1Xjk/H78OK29ykNIpRJP19AzBJVBTwLuA7YBE4BBEcYlUuHNXbWVkRPSSc/Zwqm9W/HAuX1p1ahOosMSOWDxJIIh7j7QzL4BcPdN4fgCIklpd34BT3/wHc98sIjGdWvy1KWHcWa/NjoLkEornkSQZ2bVCR8uM7MWBGcIIknn62WbGDk+nYVrtzP8sHb89qzeNK2v4yKp3OJJBH8EJgEtzexB4ALgzkijEqlgdu7J5/FpC3jxs8W0blSHF68axIk9WyY6LJEyEc9dQ6+a2SyCAeYNONfd4xpO0syGAk8SjFD2nLuPLjL/BILhKheHb0109/vijl6kHHy6aD2jJqazfOMuLj+iA7cO7UFDFYmTKqSkWkON3H2rmTUD1hIMXL93XjN337ivz4ZtqgNPA6cSDFI/w8wmu3tWkabT3f2sUm+BSES27MrjoTfn8s+Zy+nUvD7/HHEEQzofkuiwRMpcSWcEfwfOIqgrFFt8zsLpzvtZ9mBgkbtnA5jZOGAYUDQRiFQ472Su5s7X5rBhxx5uPL4LvzylG3VqqkicVE0lJYK93Ti93D23FMtuByyPmc7hfw+lxTrSzGYDK4HfuHtm0QZmNgIYAZCamlqKUETis27bbu55I5M301fRq00jnr9yEP1SGic6LJFIlZQIngQOBz4DBpZi2cXdS1e0rPXXQAd3325mZwCvAd1+8CH3scBYCAavL0UsIiVydyZ9s4L7pmSxc3cBv/lRd244vgs1q6tInFR9JSWCPDN7EUgxsz8WnRnHeAQ5QPuY6RSCo/7YZWyNeT3VzJ4xs+buvn7/oYuUjRWbd3HHpAw+nL+OgalBkbiuLVUkTpJHSYngLILSEidRuvEHZgDdzKwTsAK4GLg0toGZtQbWuLub2WCgGrChFOsSOWCFhc6rXy5l9FvzKHS4++zeXHFkRxWJk6RT0ngE64FxZjbX3Wcf6ILdPd/MbgKmEdw++oK7Z5rZjeH8MQTPJPzUzPKBXcDF7q6uH4lc9rrtjJqQwVdLNnJst+Y8NLwf7ZupSJwkJ9vXftfMbnX3R/c1ZGWihqpMS0vzmTNn7r+hSDHyCwp5dvpinnhvAXVqVOOus3pzweEpKg8hVZ6ZzXL3tOLmldQ1tPehMe11pUrIXLmFkRPSmbNiK6f1acX9w/rSUkXiRErsGnoj/P/l8gtHpOzl5hXwp/cXMuajbJrWq8WfLxvI6f3aJDoskQqjpCeL36CYLqG93P2cSCISKUOzlm7k1vHpfLduB+cPTOGus3rRpJ6KxInEKqlr6PFyi0KkjO3Ync9j0+bz8udLaNu4Li9fM5jju7dIdFgiFVJJXUMf7X0djj/Qk+AMYb677ymH2ERK5eMF67htYgYrt+ziiiM6cMvQnjSoHU+hXZHkFM9QlWcCY4DvCJ4W7mRmN7j7W1EHJ3IgtuzM4/43sxg/K4fOLerzrxuOZFDHZokOS6TCi+cw6XfAie6+CMDMugBvAkoEUmG8PWcVd72eycYde/i/E7pw88kqEicSr3gSwdq9SSCUTVCWWiTh1m7L5e7XM3lrzmp6t2nEi1cNom87FYkTORDxJIJMM5sK/IvgGsGFBGMLnAfg7hMjjE+kWO7O+Fk5PPDmXHblFXDr0B5cf2xnFYkTKYV4EkEdYA1wfDi9DmgGnE2QGJQIpFwt37iT2ydlMH3hegZ1bMro8/vTpUWDRIclUmnFM1Tl1eURiMj+FBY6r3y+hEenzceA+4b14SdDOlBNReJEDko8dw11An4OdIxtrwfKpDwtWrudURPSmbl0E8d1b8FDw/uS0lRF4kTKQjxdQ68BzwNvAIWRRiNSRF5BIWM/zubJ9xZSt1Z1fnfhAM4b2E5F4kTKUDyJINfdfzAwjUjU5qzYwq3j08latZUz+rXm3nP60qJh7USHJVLlxJMInjSzu4F3gN1733T3ryOLSpJabl4BT/5nIWM/zqZZ/VqM+cnhDO3bOtFhiVRZ8SSCfsDlBCOV7e0a8nBapEzNWLKRkePTyV6/gx+npXDHGb1pXK9mosMSqdLiSQTDgc6lqS9kZkOBJwlGKHvO3Ufvo90g4AvgIncff6Drkcpv++58Hn17Hq98vpSUpnX527VDOKZb80SHJZIU4kkEs4EmHODTxGZWHXgaOJVgIPsZZjbZ3bOKafcIwZCWkoQ+mL+WOyZmsGprLlcf3ZHf/KgH9VUkTqTcxPPX1gqYZ2Yz+P41gv3dPjoYWOTu2QBmNg4YBmQVafdzYAIwKN6gpWrYtGMP90/JYuI3K+jasgHjbzyKwzs0TXRYIkknnkRwdymX3Q5YHjOdAwyJbWBm7Qi6nk6ihERgZiOAEQCpqamlDEcqCndnasZq7p48h8078/j5SV256aSu1K6hInEiiRDPk8UfxU6b2dHApcBHxX/if02LW1yR6T8AI929oKT7wt19LDAWgsHr97NeqcDWbs3lztfm8E7WGvq1a8wr1wyhd9tGiQ5LJKnF1RFrZocS7Px/DCwm6MrZnxygfcx0CrCySJs0YFyYBJoDZ5hZvru/Fk9cUnm4O/+emcP9b2axJ7+Q207vybXHdKKGisSJJFxJYxZ3By4GLgE2AP8EzN1PjHPZM4BuYYmKFeGyLo1t4O6dYtb3EjBFSaDqWb5xJ7dNzOCTResZ3KkZo8/rR2cViROpMEo6I5gHTAfOjhmU5lfxLtjd883sJoK7gaoDL7h7ppndGM4fU/qwpTIoKHRe/mwJj02bT/VqxgPn9uXSwakqEidSwZSUCM4nOIr/wMzeBsZRfL//Prn7VGBqkfeKTQDuftWBLFsqtoVrtnHrhHS+WbaZE3q04KHh/WjbpG6iwxKRYpQ0eP0kYJKZ1QfOBX4FtDKzPwOT3P2d8glRKpM9+YWM+eg7nnp/EfVrV+cPFx3KsEPbqkicSAUWz11DO4BXgVfNrBnBCGWjCGoPifxXes5mbh2fzrzV2zh7QFvuPrs3zRuoSJxIRXdAj2+6+0bgL+E/ESAoEvfEuwt4dno2LRrW5tkr0ji1d6tEhyUicdJz/HJQvsjewKgJ6SzZsJNLBrdn1Om9aFxXReJEKhMlAimVbbl5jH5rHq9+uYzUZvX4+3VDOKqrisSJVEZKBHLA3p+3hjsmzWHN1lyuO6YT/+9H3alXS79KIpWV/nolbht37OG+NzJ57duVdG/VgGcuO4rDUlUkTqSyUyKQ/XJ33khfxT2TM9mWm8cvTu7Gz07sSq0aKg8hUhUoEUiJVm8JisS9N3cNA1Ia88gFQ+jZWkXiRKoSJQIplrszbsZyHnpzLnmFhdxxRi+uOaYT1VUeQqTKUSKQH1i6YQejJmTwefYGjujcjNHn9adj8/qJDktEIqJEIP9VUOi8+OliHn9nPjWrVeOh4f24eFB7FYkTqeKUCASA+auDInGzl2/m5J4teWB4X9o0VpE4kWSgRJDk9uQX8syHi3j6g0U0rFOTP15yGGf3b6MicSJJRIkgiX27fDMjx6czf802hh3alrvP7kOz+rUSHZaIlDMlgiS0a08Bv3tnPi98upiWDevw/JVpnNxLReJEklWkicDMhgJPEoxQ9py7jy4yfxhwP1AI5AO/dPdPoowp2X323XpGTchg2cadXDoklVGn96RRHRWJE0lmkSUCM6sOPA2cSjCQ/Qwzm+zuWTHN/gNMdnc3s/7Av4CeUcWUzLbm5vHw1Ln846vldDikHv+4/giO7HJIosMSkQogyjOCwcAid88GMLNxwDDgv4nA3bfHtK8PeITxJK33stZwx2sZrNu2mxuO68wvT+lO3VrVEx2WiFQQUSaCdsDymOkcYEjRRmY2HHgYaAmcWdyCzGwEMAIgNTW1zAOtqjZs3809b2TxxuyV9GzdkGevSKN/SpNEhyUiFUyUiaC4+w9/cMQfMzbycQTXC04pps1YYCxAWlqazhr2w915/duV3PtGJtt35/P/Tu3Ojcd3UZE4ESlWlIkgB2gfM50CrNxXY3f/2My6mFlzd18fYVxV2srNu7jztTm8P28th7ZvwqMX9Kd7q4aJDktEKrAoE8EMoJuZdQJWABcDl8Y2MLOuwHfhxeKBQC1gQ4QxVVmFhc7fv1rG6LfmUVDo3HVWb646qqOKxInIfkWWCNw938xuAqYR3D76grtnmtmN4fwxwPnAFWaWB+wCLnJ3df0coMXrdzBqQjpfLt7I0V0P4eHh/Uk9pF6iwxKRSsIq2343LS3NZ86cmegwKoT8gkKe/2Qxv393AbVqVOOuM3tzYVqKykOIyA+Y2Sx3Tytunp4srqSyVm5l5IR0MlZs4dTerXjg3L60alQn0WGJSCWkRFDJ7M4v4Kn3F/HnD7+jSb2aPH3pQM7o11pnASJSakoElcispZsYOSGdRWu3c95h7bjrrN40VZE4ETlISgSVwM49+Tw2bT4vfbaENo3q8OLVgzixR8tEhyUiVYQSQQX3ycL1jJqYTs6mXVx+RAduHdqDhioSJyJlSImggtqyK48H38ziXzNz6NS8Pv+64UgGd2qW6LBEpApSIqiApmWu5q7X5rBhxx5+ekIXfnFyN+rUVJE4EYmGEkEFsm7bbu6ZnMmbGavo1aYRz185iH4pjRMdlohUcUoEFYC7M/HrFdw3JYtdewq45bQejDiuMzWrq0iciERPiSDBVmzexe0TM/howToGpgZF4rq2VJE4ESk/SgQJUljo/O3LpTzy1jwcuOfs3lx+pIrEiUj5UyJIgO/WbWfUhHRmLNnEsd2a89DwfrRvpiJxIpIYSgTlKK+gkGenZ/OH9xZSp0Y1HrugPxccriJxIpJYSgTlZM6KLYyckE7myq0M7dOa+87tQ8uGKhInIomnRBCx3LwC/vT+QsZ8lE3TerX482UDOb1fm0SHJSLyX0oEEZq5ZCO3Tkgne90Ozh+Ywl1n9aJJPRWJE5GKJdIb1c1sqJnNN7NFZjaqmPmXmVl6+O8zMxsQZTzlZcfufO6ZnMmFf/mc3XmFvHzNYH734wFKAiJSIUV2RmBm1YGngVMJBrKfYWaT3T0rptli4Hh332RmpwNjgSFRxVQePlqwjtsnZrByyy6uPLIjt5zWg/q1deIlIhVXlHuowcAid88GMLNxwDDgv4nA3T+Laf8FkBJhPJHavHMP90+Zy4Svc+jcoj7/vuFI0jqqSJyIVHxRJoJ2wPKY6RxKPtq/FniruBlmNgIYAZCamlpW8ZWZtzJWcdfrmWzauYefndiFn5+kInEiUnlEmQiKuznei21odiJBIjimuPnuPpag24i0tLRil5EIa7fm8tvXM3k7czV92jbi5WsG0aetisSJSOUSZSLIAdrHTKcAK4s2MrP+wHPA6e6+IcJ4yoy7M35WDvdPySI3v5CRQ3ty3bGdVCRORCqlKBPBDKCbmXUCVgAXA5fGNjCzVGAicLm7L4gwljKzfONObp+UwfSF6xnUsSmjz+9PlxYNEh2WiEipRZYI3D3fzG4CpgHVgRfcPdPMbgznjwF+CxwCPBOWWch397SoYjoYBYXOXz9fwqPT5mPA/cP6cNmQDlRTkTgRqeTMvcJ0ucclLS3NZ86cWa7rXLR2GyMnZDBr6SaO796CB4f3JaWpisSJSOVhZrP2daCtG9xLkFdQyF8++o4//mcR9WpX5/c/HsDww9qpSJyIVClKBPswZ8UWbhmfztxVWzmzXxvuOacPLRrWTnRYIiJlTomgiNy8Av7w3kKenZ5Ns/q1GPOTwxnat3WiwxIRiYwSQYyvFm9k1IR0stfv4KK09tx+Ri8a16uZ6LBERCKlRABsy83j0bfn89cvlpLStC5/u3YIx3RrnuiwRETKRdIngg/mr+WOiRms2prLNUd34jendaderaT/WkQkiSTtHm/Tjj3cPyWLid+soGvLBoy/8SgO79A00WGJiJS7pEsE7s6bGau4+/VMtuzK4+aTuvKzk7pSu4aKxIlIckqqRLBmay53vTaHd7LW0K9dY/523RB6tWmU6LBERBIqaRLBB/PWcvO4b9iTX8htp/fk2mM6UUNF4kREkicRdGpen4GpTbnnnD50al4/0eGIiFQYSZMIOjavz8vXDE50GCIiFY76RkREkpwSgYhIklMiEBFJckoEIiJJLtJEYGZDzWy+mS0ys1HFzO9pZp+b2W4z+02UsYiISPEiu2vIzKoDTwOnEgxkP8PMJrt7VkyzjcDNwLlRxSEiIiWL8oxgMLDI3bPdfQ8wDhgW28Dd17r7DCAvwjhERKQEUSaCdsDymOmc8L0DZmYjzGymmc1ct25dmQQnIiKBKB8oK25gXy/Ngtx9LDAWwMzWmdnSUsbUHFhfys9WVtrm5KBtTg4Hs80d9jUjykSQA7SPmU4BVh7sQt29RWk/a2Yz3T3tYGOoTLTNyUHbnByi2uYou4ZmAN3MrJOZ1QIuBiZHuD4RESmFyM4I3D3fzG4CpgHVgRfcPdPMbgznjzGz1sBMoBFQaGa/BHq7+9ao4hIRke+LtOicu08FphZ5b0zM69UEXUblZWw5rqui0DYnB21zcohkm829VNdvRUSkilCJCRGRJKdEICKS5KpkIjCzF8xsrZnN2cd8M7M/hjWQ0s1sYHnHWJbi2N7Lwu1MN7PPzGxAecdY1va3zTHtBplZgZldUF6xRSWebTazE8zsWzPLNLOPyjO+KMTxu93YzN4ws9nhNl9d3jGWNTNrb2YfmNnccJt+UUybMt2HVclEALwEDC1h/ulAt/DfCODP5RBTlF6i5O1dDBzv7v2B+6kaF9leouRt3lvv6hGCO9eqgpcoYZvNrAnwDHCOu/cBLiyfsCL1EiX/nH8GZLn7AOAE4Hfh7eqVWT7wa3fvBRwB/MzMehdpU6b7sCqZCNz9Y4KCdvsyDHjFA18ATcysTflEV/b2t73u/pm7bwonv6B879SKRBw/Y4CfAxOAtdFHFL04tvlSYKK7LwvbV/rtjmObHWhoZgY0CNvml0dsUXH3Ve7+dfh6GzCXH5bnKdN9WJVMBHEoszpIldC1wFuJDiJqZtYOGA6M2V/bKqQ70NTMPjSzWWZ2RaIDKgdPAb0IqhZkAL9w98LEhlR2zKwjcBjwZZFZZboPS5rB64soszpIlYmZnUiQCI5JdCzl4A/ASHcvCA4Wk0IN4HDgZKAu8LmZfeHuCxIbVqROA74FTgK6AO+a2fSq8FCqmTUgOKP9ZTHbU6b7sGRNBJHUQarIzKw/8BxwurtvSHQ85SANGBcmgebAGWaW7+6vJTSqaOUA6919B7DDzD4GBgBVORFcDYz24IGoRWa2GOgJfJXYsA6OmdUkSAKvuvvEYpqU6T4sWbuGJgNXhFfejwC2uPuqRAcVFTNLBSYCl1fxo8P/cvdO7t7R3TsC44H/q+JJAOB14Fgzq2Fm9YAhBP3LVdkygjMgzKwV0APITmhEBym83vE8MNfdf7+PZmW6D6uSZwRm9g+COwiam1kOcDdQE/5b4mIqcAawCNhJcFRRacWxvb8FDgGeCY+Q8yt71cY4trnK2d82u/tcM3sbSAcKgefcvcTbayu6OH7O9wMvmVkGQXfJSHev7KWpjwYuBzLM7NvwvduBVIhmH6YSEyIiSS5Zu4ZERCSkRCAikuSUCEREkpwSgYhIklMiEBFJckoEIvtgZoeElTy/NbPVZrYifL3dzJ5JdHwiZUW3j4rEwczuAba7++OJjkWkrOmMQOQAhTX/p4Sv7zGzl83sHTNbYmbnmdmjZpZhZm+HpQIws8PN7KOwGNy0ylztVqoeJQKRg9cFOJOgNPDfgA/cvR+wCzgzTAZ/Ai5w98OBF4AHExWsSFFVssSESDl7y93zwjIH1YG3w/czgI4E9W/6ElTGJGxTZWtbSeWjRCBy8HYDuHuhmeX5/y68FRL8jRmQ6e5HJipAkZKoa0gkevOBFmZ2JAQlhs2sT4JjEvkvJQKRiLn7HuAC4BEzm00wkMpRCQ1KJIZuHxURSXI6IxARSXJKBCIiSU6JQEQkySkRiIgkOSUCEZEkp0QgIpLklAhERJLc/we7bR4hZ39R2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "time_values = []\n",
    "amplified_values = []\n",
    "\n",
    "while True:\n",
    "    # Read the amplified value from the system (replace this with your actual reading code)\n",
    "    amplified_value = random.random()\n",
    "\n",
    "    # Termination condition (e.g., check if the system has stopped generating signals)\n",
    "    if amplified_value < 0.1:\n",
    "        break\n",
    "\n",
    "    # Update the time and amplified value lists\n",
    "    time_values.append(len(time_values) + 1)  # Assuming time increments by 1\n",
    "    amplified_values.append(amplified_value)\n",
    "\n",
    "# Plot the graph\n",
    "plt.plot(time_values, amplified_values)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Amplified Value')\n",
    "plt.title('Amplified Gas Pressure Values')\n",
    "\n",
    "# Display the graph\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d9968cc",
   "metadata": {},
   "source": [
    "This code seems to be simulating the amplification of gas pressure values over time. <br>\n",
    "The while loop continues indefinitely until a termination condition is met. In this case, the loop breaks when the amplified value falls below 0.1. Inside the loop, a random value between 0 and 1 is generated to simulate the amplified value.\n",
    "\n",
    "The time and amplified values are stored in separate lists ('time_values' and 'amplified_values') for plotting purposes. The time values are incremented by 1 for each iteration of the loop.\n",
    "\n",
    "After the loop ends, the graph is plotted using 'plt.plot()' with 'time_values' on the x-axis and 'amplified_values' on the y-axis. The x-axis is labeled as \"Time,\" the y-axis is labeled as \"Amplified Value,\" and the title of the graph is set as \"Amplified Gas Pressure Values.\" Finally, 'plt.show()' is called to display the graph."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
